Multi-stage processes are relied upon in research and manufacturing of a wide range of products including biologics, pharmaceuticals, mechanical devices, electrical devices, and food, to name a few examples. Unfortunately, such processes typically have many sources of variation. While most of these sources are minor and may be ignored, the dominant sources of variation may adversely affect the efficiency or even viability of such processes. If identified, however, resources to remove these dominant sources of variation can be engaged and, potentially, such dominant sources of variation can be removed, minimized or contained. Once these dominant sources of variation are addressed, a process may be considered stabilized. When a process is stable, its variation should remain within a known set of limits. That is, at least, until another assignable source of variation occurs. For example, a laundry soap packaging line may be designed to fill each laundry soap box with fourteen ounces of laundry soap. Some boxes will have slightly more than fourteen ounces, and some will have slightly less. When the package weights are measured, the data will demonstrate a distribution of net weights. If the production process, its inputs, or its environment (for example, the machines on the line) change, the distribution of the data will change. For example, as the cams and pulleys of the machinery wear, the laundry soap filling machine may put more than the specified amount of soap into each box. Although this might benefit the customer, from the manufacturer's point of view, this is wasteful and increases the cost of production. If the manufacturer finds the change and its source in a timely manner, the change can be corrected (for example, the cams and pulleys replaced),
While identification of variation of processes is nice in theory, in practice there are many barriers to finding such variation. Most processes combine many different functional components each with their own data forms and types of errors. For instance, a process for manufacturing a synthetic compound using a cell culture combines chemical components, biological components, fermentation components, and industrial equipment components. Each of these components involves different units of quantification, measurement, and error. As such, the rate-limiting step for developing and stabilizing processes is not development of the algorithms that are used in such processes; it is the acquisition and contextualizing of the data in such processes. This requires data aggregation and reproducibility assessment across many disparate systems and functionalities so that scientific reasoning is based on reproducible data rather than on artifacts of noise and uncertainty. Conventional systems fail to deliver adequate capabilities for such analysis. They focus on storing files and data without providing the structure, context or flexibility to enable real-time analytics and feedback to the user.
For instance, electronic lab notebooks (ELNs) are basically “paper on glass” and have inadequate ability to streamline longitudinal analytics across studies. Lab information management systems (LIMS) focus on sample data collection, but don't provide the protocol or study context to facilitate analytics, nor the flexibility to adapt to changing workflows “on-the-fly” and the many disparate functionalities that are often found in processes. Thus the relationship between protocol and outcome remains unclear or even inaccessible and information systems become “dead” archives of old work mandated by institutional policies rather than assets that drive process stabilization.
As a result, billions of dollars are lost each year on material and life science research that are not stabilized and thus have unsatisfactory reproducibility rates. Moreover, the incidence of multi-million dollar failures during process transfer to manufacturing remains high. Thus, given the above background, what is needed in the art are improved systems and methods for process design and analysis of processes that result in their stabilization.